An Energy-Efficient Radio Resource Allocation Algorithm for Heterogeneous Wireless Networks

The heterogeneous deployment of ultra-dense femtocells overlaying the conventional macrocells is seen as an efficient and cost-effective solution for providing indoor coverage and capacity in the fifth generation (5G) wireless networks. However, it will result in interference between macrocells and femtocells (cross-tier interference), interference between neighbouring femtocells (co-tier interference) and significant increase in the total power consumption. Most of the previous works on radio resource allocation (RRA) in the literature focus on spectrum sharing and interference management, they do not fully address the aspect of energy-efficiency (EE). Motivated by these limitations, in this paper, we present an energy-efficient radio resource allocation algorithm (EERRAA) for interference management, throughput maximization and energy-efficiency to enhance the performance of the overall network, based on cognitive radio technology and stochastic process. The energy-efficient radio resource allocation problem is formulated as a multi-objective optimization problem which belongs to the class of mixed integer nonlinear programming (MINLP). The problem is transformed to a linear programming and solved using the Lagrangian dual decomposition method. The performance of the EERRAA is evaluated using MATLAB. Simulation results show that the proposed algorithm reduces interference, improves the throughput and energy-efficiency of the overall network when compared with other existing algorithms.